Redefining Cloud Efficiency - Improved Performance, Resiliency & Costs
- Mark Thompson
- 1 day ago
- 5 min read
Introduction
Enterprises today are under relentless pressure to accelerate digital initiatives, launch new services, and scale operations globally. But this speed often carries a hidden cost -bloated cloud estates. Over-provisioned virtual machines, underutilized databases, and misaligned environments silently drain millions from IT budgets. What seems like agility on the surface can quickly turn into financial waste that erodes margins and limits innovation.
This whitepaper discusses how Fabrics solves this problem.
Executive Summary
SMBs and enterprises alike struggle with a fundamental mismatch: cloud capacity is provisioned for peak demand, yet real-world usage fluctuates constantly. The result is over‑provisioned infrastructure and wasted spending or under-provisioned infrastructure and performance issues.
Fabrics tackles this head‑on with Micro-Dynamic Optimization: connecting three variables - demand, capacity, and performance - into a feedback loop. Every five minutes, Fabrics pulls utilization metrics (CPU, memory, storage, etc.) as a proxy for performance, compares them to custom utilization targets, and automatically adjusts capacity up, down or out. Machine learning and predictive analytics anticipate usage patterns, ensuring elastic capacity before performance impacts arise.
74% Orgs Exceeding Cloud Budgets
74% of organizations exceed their cloud budget
Overspend often reaching 23-40% beyond planned allocations.
Source: Flexera 2024 State of the Cloud Report
35% Cloud Spend on Idle Resources
Idle resources account for 35-50% of wasted cloud spend
Driven by over-provisioned VMs, unused dev/test environments, and underutilized databases.
Source: Gartner
Cloud Costs Growing
> 20.4% On Avg. YoY
Cloud costs are projected to grow 20.4% YoY, outpacing growth in most IT budgets
Making optimization a financial and strategic imperative.
Source: IDC & Gartner
70% Decision Makers Lack Visibility
70% of cloud decision-makers admit their teams lack visibility into real-time cloud usage, leading to under informed scaling decisions.
Source: Forrester
Use Cases
The challenges of managing cloud infrastructure aren't theoretical, they’re daily, operational realities for organizations of all sizes. Whether it’s scaling development environments, handling unpredictable backend traffic, or maintaining high-performance virtual desktops, cloud inefficiencies can quietly erode both budget and velocity.
Fabrics was designed to solve these problems where they occur. The following use cases illustrate how Fabrics applies dynamic optimization to deliver measurable results across the startup stack—from dev teams to end users.
Use Case 1: Azure App Service Plan Optimization
Challenge:
Azure’s native autoscaling has important blind spots. It can only be scaled within the constraints of a given SKU, which means organizations often overpay for compute capacity they don’t fully use. For example, if workloads peak at one tier but drop to a much lower baseline during nights or weekends, the platform won’t automatically downgrade to cheaper tiers. Engineers typically build custom scripts to manage this, but those are brittle, high-maintenance, and error-prone.
Solution:
Fabrics removes these artificial limits by micro-dynamically moving workloads between SKU tiers in real time. That means you don’t just scale up when you need performance, you also scale down when demand drops. Imagine a SaaS business that experiences high weekday activity but very little weekend load. With Fabrics, their app service plans automatically downgrade during downtime, slashing spending while ensuring the relevant high performance tier is available when traffic returns. This delivers true elasticity that Azure alone doesn’t provide.
This happens automatically, without disrupting developer workflows or requiring constant DevOps oversight.
Additionally, Fabrics automates routine maintenance tasks such as overnight environment restarts, right-sizing instances after usage changes, and eliminating zombie resources. Dev teams benefit from a seamless experience, while finance teams benefit from measurable cost control.
Use Case 2: AVD User Density & Storage Elimination
Challenge:
Traditional Azure Virtual Desktop (AVD) environments create persistent cost problems. Even when VMs are powered down, their underlying storage disks continue to rack up charges. For organizations with hundreds of desktops, this leads to hidden “shadow” costs - resources that aren’t actively providing value but are still billed every hour. IT teams often try manual cleanup, but that’s disruptive to users and risks data loss.
Solution:
Fabrics automates this by predicting idle patterns (for example, nights or after a seasonal project ends). When users log off, Fabrics destroys the VMs entirely eliminating both compute and storage costs. When users log back in, VMs are instantly rehydrated, restoring a seamless desktop experience. This not only slashes waste in Azure costs but also ensures that IT staff don’t waste cycles writing scripts, monitoring sessions, or manually managing resource lifecycles; allowing more resources to be focused on delivering strategic projects.
Use Case 3: Predicative, ML-Driven Autoscaling
Challenge:
Most cloud scaling today is reactive. It waits until utilization thresholds (CPU, memory, etc.) are exceeded before launching new instances. By the time those extra resources kick in, users may already have experienced slowness or interruption. For fast-growing startups or companies running high-profile product launches, these micro-delays can translate into churn, lost transactions, and damaged brand reputation.
Solution:
Fabrics flips the script by applying machine learning models that study historical usage patterns. Instead of waiting for a traffic surge to overwhelm capacity, Fabrics anticipates the spike. For example, if a retailer knows a flash sale or marketing campaign is scheduled for Friday at noon, Fabrics will proactively scale out before the campaign begins - guaranteeing smooth performance when demand hits. This predictive approach eliminates the lag and ensures customer experience; company revenues are not negatively impacted.
Use Case 4: Intelligent VM SKU Rightsizing
Challenge:
Cloud teams often choose VM SKUs based on guesswork or outdated sizing calculators. The result? Many workloads are stuck in general-purpose instances that don’t align with their true performance needs. A memory-intensive database might be CPU-underutilized but memory-starved, while a compute-heavy analytics job might be overpaying for unused RAM. These mismatches silently drain budgets, often by double-digit percentages.
Solution:
Fabrics continuously profiles each VM’s resource usage to identify inefficiencies. If it detects that a workload is better suited for a memory-optimized or compute-optimized SKU, it automatically migrates the workload to the right tier without downtime or manual intervention. The result is a perfect “fit” between workload and resource, ensuring every dollar spent directly drives performance. Over time, this creates compounding savings and eliminates the need for constant manual tuning by IT teams.
Fabrics Truly Defines Cloud Efficiency
We hear it often: "We’re already optimized"
But here's the reality - most organizations aren’t. What they have is static provisioning, basic alerting, or traditional autoscaling that reacts too late. Fabrics works differently. It doesn't just identify idle infrastructure - it acts on it. Fabrics automates adjusts for compute, memory, and instance types based on real-time demand, eliminating drift and waste that conventional tools miss.
Traditional autoscaling stops at recommendations or basic thresholds. Fabrics goes further - automating capacity adjustments in real time, informed by performance‑driving metrics and predictive insights. The result is a leaner infrastructure that flexes with demand, extending runway, accelerating innovation, and cutting operational overhead.
True optimization isn’t about having dashboards. It’s about automation that adapts continuously. If you haven’t seen 20-40% cost savings positive performance improvements, you're not done optimizing.
Challenge Us: See Fabrics in Action
Enable the Fabrics platform to observe how your cloud resources automatically align with real usage, then disable it to compare results. The difference in efficiency and resource optimization is clear - explore the impact of elastic capacity management firsthand.
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